Haitao Liao
Dr. Haitao Liao is an Associate Professor in the Systems and Industrial Engineering Department at the University of Arizona (UofA), Tucson, Arizona. He is also the Director of Reliability & Intelligent Systems Engineering (RISE) Laboratory at UofA (http://www.sie.arizona.edu/reliability-intelligent-systems-engineering-laboratory). He received his Ph.D. in Industrial and Systems Engineering from Rutgers University, New Jersey. His research interests focus on modeling of accelerated testing, probabilistic risk assessment, maintenance models and optimization, service part inventory control, and prognostics. His current research is sponsored by the U.S. National Science Foundation, Department of Energy, and industry. He was a recipient of the National Science Foundation CAREER Award in 2010. He is a member of IIE, INFORMS, and SRE.

Haitao Liao
Dr. Haitao Liao is an Associate Professor in the Systems and Industrial Engineering Department at the University of Arizona (UofA), Tucson, Arizona. He is also the Director of Reliability & Intelligent Systems Engineering (RISE) Laboratory at UofA (http://www.sie.arizona.edu/reliability-intelligent-systems-engineering-laboratory). He received his Ph.D. in Industrial and Systems Engineering fro...read more

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Accelerated life testing (ALT) is widely used to expedite failures of a product in a short time period for predicting the product’s reliability under normal operating conditions. The resulting ALT data are often characterized by a probability distribution, such as Weibull, Lognormal, Gamma distribution, along with a life-stress relationship. However, if the selected failure time distribution is not adequate in describing the ALT data, the resulting reliability prediction would be misleading. In this talk, we provide a generic method for modeling ALT data which will assist engineers in dealing with a variety of failure time distributions. The method uses Erlang-Coxian (EC) distributions, which belong to a particular subset of phase-type (PH) distributions, to approximate the underlying failure time distributions arbitrarily closely. To estimate the parameters of such an EC-based ALT model, two statistical inference approaches are proposed. First, a mathematical programming approach is formulated to simultaneously match the moments of the EC-based ALT model to the ALT data collected at all test stress levels. This approach resolves the feasibility issue of the method of moments. In addition, the maximum likelihood estimation (MLE) approach is proposed to handle ALT data with type-I censoring. Numerical examples are provided to illustrate the capability of the generic method in modeling ALT data.

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